Articles | Volume 13, issue 3
https://doi.org/10.5194/esd-13-1289-2022
https://doi.org/10.5194/esd-13-1289-2022
Research article
 | 
06 Sep 2022
Research article |  | 06 Sep 2022

Combining machine learning and SMILEs to classify, better understand, and project changes in ENSO events

Nicola Maher, Thibault P. Tabarin, and Sebastian Milinski

Related authors

Improving statistical projections of ocean dynamic sea-level change using pattern recognition techniques
Víctor Malagón-Santos, Aimée B. A. Slangen, Tim H. J. Hermans, Sönke Dangendorf, Marta Marcos, and Nicola Maher
Ocean Sci., 19, 499–515, https://doi.org/10.5194/os-19-499-2023,https://doi.org/10.5194/os-19-499-2023, 2023
Short summary
The future of the El Niño–Southern Oscillation: using large ensembles to illuminate time-varying responses and inter-model differences
Nicola Maher, Robert C. Jnglin Wills, Pedro DiNezio, Jeremy Klavans, Sebastian Milinski, Sara C. Sanchez, Samantha Stevenson, Malte F. Stuecker, and Xian Wu
Earth Syst. Dynam., 14, 413–431, https://doi.org/10.5194/esd-14-413-2023,https://doi.org/10.5194/esd-14-413-2023, 2023
Short summary
The sensitivity of the El Niño–Southern Oscillation to volcanic aerosol spatial distribution in the MPI Grand Ensemble
Benjamin Ward, Francesco S. R. Pausata, and Nicola Maher
Earth Syst. Dynam., 12, 975–996, https://doi.org/10.5194/esd-12-975-2021,https://doi.org/10.5194/esd-12-975-2021, 2021
Short summary
Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble
Nicola Maher, Sebastian Milinski, and Ralf Ludwig
Earth Syst. Dynam., 12, 401–418, https://doi.org/10.5194/esd-12-401-2021,https://doi.org/10.5194/esd-12-401-2021, 2021
How large does a large ensemble need to be?
Sebastian Milinski, Nicola Maher, and Dirk Olonscheck
Earth Syst. Dynam., 11, 885–901, https://doi.org/10.5194/esd-11-885-2020,https://doi.org/10.5194/esd-11-885-2020, 2020
Short summary

Related subject area

Earth system change: climate scenarios
Countries most exposed to individual and concurrent extremes and near-permanent extreme conditions at different global warming levels
Fulden Batibeniz, Mathias Hauser, and Sonia Isabelle Seneviratne
Earth Syst. Dynam., 14, 485–505, https://doi.org/10.5194/esd-14-485-2023,https://doi.org/10.5194/esd-14-485-2023, 2023
Short summary
Direct and indirect application of univariate and multivariate bias corrections on heat-stress indices based on multiple regional-climate-model simulations
Liying Qiu, Eun-Soon Im, Seung-Ki Min, Yeon-Hee Kim, Dong-Hyun Cha, Seok-Woo Shin, Joong-Bae Ahn, Eun-Chul Chang, and Young-Hwa Byun
Earth Syst. Dynam., 14, 507–517, https://doi.org/10.5194/esd-14-507-2023,https://doi.org/10.5194/esd-14-507-2023, 2023
Short summary
Overview: The Baltic Earth Assessment Reports (BEAR)
H. E. Markus Meier, Marcus Reckermann, Joakim Langner, Ben Smith, and Ira Didenkulova
Earth Syst. Dynam., 14, 519–531, https://doi.org/10.5194/esd-14-519-2023,https://doi.org/10.5194/esd-14-519-2023, 2023
Short summary
The implications of maintaining Earth's hemispheric albedo symmetry for shortwave radiative feedbacks
Aiden R. Jönsson and Frida A.-M. Bender
Earth Syst. Dynam., 14, 345–365, https://doi.org/10.5194/esd-14-345-2023,https://doi.org/10.5194/esd-14-345-2023, 2023
Short summary
Robust global detection of forced changes in mean and extreme precipitation despite observational disagreement on the magnitude of change
Iris Elisabeth de Vries, Sebastian Sippel, Angeline Greene Pendergrass, and Reto Knutti
Earth Syst. Dynam., 14, 81–100, https://doi.org/10.5194/esd-14-81-2023,https://doi.org/10.5194/esd-14-81-2023, 2023
Short summary

Cited articles

An, S.-I. and Wang, B.: Interdecadal Change of the Structure of the ENSO Mode and Its Impact on the ENSO Frequency, J. Climate, 13, 2044–2055, https://doi.org/10.1175/1520-0442(2000)013<2044:ICOTSO>2.0.CO;2, 2000. a, b
Ashok, K., Behera, S. K., Rao, S. A., Weng, H., and Yamagata, T.: El Niño Modoki and its possible teleconnection, J. Geophys. Res.-Oceans, 112, C11007, https://doi.org/10.1029/2006JC003798, 2007. a
Barnes, E. A., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C., and Anderson, D.: Viewing Forced Climate Patterns Through an AI Lens, Geophys. Res. Lett., 46, 13389–13398, https://doi.org/10.1029/2019GL084944, 2019. a
Barnes, E. A., Toms, B., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C., and Anderson, D.: Indicator Patterns of Forced Change Learned by an Artificial Neural Network, J. Adv. Model. Earth Sy., 12, e2020MS002195, https://doi.org/10.1029/2020MS002195, 2020. a
Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M., and Vialard, J.: ENSO representation in climate models: from CMIP3 to CMIP5, Clim. Dynam., 42, 1999–2018, https://doi.org/10.1007/s00382-013-1783-z, 2014. a
Download
Short summary
El Niño events occur as two broad types: eastern Pacific (EP) and central Pacific (CP). EP and CP events differ in strength, evolution, and in their impacts. In this study we create a new machine learning classifier to identify the two types of El Niño events using observed sea surface temperature data. We apply our new classifier to climate models and show that CP events are unlikely to change in frequency or strength under a warming climate, with model disagreement for EP events.
Altmetrics
Final-revised paper
Preprint